/aws-solution-architect

name: "aws-solution-architect"
description: Design AWS architectures for startups using serverless patterns and IaC templates. Use when asked to design serverless architecture, create CloudFormation templates, optimize AWS costs, set up CI/CD pipelines, or migrate to AWS. Covers Lambda, API Gateway, DynamoDB, ECS, Aurora, and cost optimization.

AWS Solution Architect

Design scalable, cost-effective AWS architectures for startups with infrastructure-as-code templates.

---

Workflow

Step 1: Gather Requirements

Collect application specifications:

- Application type (web app, mobile backend, data pipeline, SaaS)
- Expected users and requests per second
- Budget constraints (monthly spend limit)
- Team size and AWS experience level
- Compliance requirements (GDPR, HIPAA, SOC 2)
- Availability requirements (SLA, RPO/RTO)

Step 2: Design Architecture

Run the architecture designer to get pattern recommendations:

python scripts/architecture_designer.py --input requirements.json

**Example output:**

{
  "recommended_pattern": "serverless_web",
  "service_stack": ["S3", "CloudFront", "API Gateway", "Lambda", "DynamoDB", "Cognito"],
  "estimated_monthly_cost_usd": 35,
  "pros": ["Low ops overhead", "Pay-per-use", "Auto-scaling"],
  "cons": ["Cold starts", "15-min Lambda limit", "Eventual consistency"]
}

Select from recommended patterns:

• **Serverless Web**: S3 + CloudFront + API Gateway + Lambda + DynamoDB
• **Event-Driven Microservices**: EventBridge + Lambda + SQS + Step Functions
• **Three-Tier**: ALB + ECS Fargate + Aurora + ElastiCache
• **GraphQL Backend**: AppSync + Lambda + DynamoDB + Cognito

See `references/architecture_patterns.md` for detailed pattern specifications.

**Validation checkpoint:** Confirm the recommended pattern matches the team's operational maturity and compliance requirements before proceeding to Step 3.

Step 3: Generate IaC Templates

Create infrastructure-as-code for the selected pattern:

# Serverless stack (CloudFormation)
python scripts/serverless_stack.py --app-name my-app --region us-east-1

**Example CloudFormation YAML output (core serverless resources):**

AWSTemplateFormatVersion: '2010-09-09'
Transform: AWS::Serverless-2016-10-31

Parameters:
  AppName:
    Type: String
    Default: my-app

Resources:
  ApiFunction:
    Type: AWS::Serverless::Function
    Properties:
      Handler: index.handler
      Runtime: nodejs20.x
      MemorySize: 512
      Timeout: 30
      Environment:
        Variables:
          TABLE_NAME: !Ref DataTable
      Policies:
        - DynamoDBCrudPolicy:
            TableName: !Ref DataTable
      Events:
        ApiEvent:
          Type: Api
          Properties:
            Path: /{proxy+}
            Method: ANY

  DataTable:
    Type: AWS::DynamoDB::Table
    Properties:
      BillingMode: PAY_PER_REQUEST
      AttributeDefinitions:
        - AttributeName: pk
          AttributeType: S
        - AttributeName: sk
          AttributeType: S
      KeySchema:
        - AttributeName: pk
          KeyType: HASH
        - AttributeName: sk
          KeyType: RANGE

> Full templates including API Gateway, Cognito, IAM roles, and CloudWatch logging are generated by `serverless_stack.py` and also available in `references/architecture_patterns.md`.

**Example CDK TypeScript snippet (three-tier pattern):**

import * as ecs from 'aws-cdk-lib/aws-ecs';
import * as ec2 from 'aws-cdk-lib/aws-ec2';
import * as rds from 'aws-cdk-lib/aws-rds';

const vpc = new ec2.Vpc(this, 'AppVpc', { maxAzs: 2 });

const cluster = new ecs.Cluster(this, 'AppCluster', { vpc });

const db = new rds.ServerlessCluster(this, 'AppDb', {
  engine: rds.DatabaseClusterEngine.auroraPostgres({
    version: rds.AuroraPostgresEngineVersion.VER_15_2,
  }),
  vpc,
  scaling: { minCapacity: 0.5, maxCapacity: 4 },
});

Step 4: Review Costs

Analyze estimated costs and optimization opportunities:

python scripts/cost_optimizer.py --resources current_setup.json --monthly-spend 2000

**Example output:**

{
  "current_monthly_usd": 2000,
  "recommendations": [
    { "action": "Right-size RDS db.r5.2xlarge → db.r5.large", "savings_usd": 420, "priority": "high" },
    { "action": "Purchase 1-yr Compute Savings Plan at 40% utilization", "savings_usd": 310, "priority": "high" },
    { "action": "Move S3 objects >90 days to Glacier Instant Retrieval", "savings_usd": 85, "priority": "medium" }
  ],
  "total_potential_savings_usd": 815
}

Output includes:

• Monthly cost breakdown by service
• Right-sizing recommendations
• Savings Plans opportunities
• Potential monthly savings

Step 5: Deploy

Deploy the generated infrastructure:

# CloudFormation
aws cloudformation create-stack \
  --stack-name my-app-stack \
  --template-body file://template.yaml \
  --capabilities CAPABILITY_IAM

# CDK
cdk deploy

# Terraform
terraform init && terraform apply

Step 6: Validate and Handle Failures

Verify deployment and set up monitoring:

# Check stack status
aws cloudformation describe-stacks --stack-name my-app-stack

# Set up CloudWatch alarms
aws cloudwatch put-metric-alarm --alarm-name high-errors ...

**If stack creation fails:**

1. Check the failure reason:

   aws cloudformation describe-stack-events \
     --stack-name my-app-stack \
     --query 'StackEvents[?ResourceStatus==`CREATE_FAILED`]'

2. Review CloudWatch Logs for Lambda or ECS errors. 3. Fix the template or resource configuration. 4. Delete the failed stack before retrying:

   aws cloudformation delete-stack --stack-name my-app-stack
   # Wait for deletion
   aws cloudformation wait stack-delete-complete --stack-name my-app-stack
   # Redeploy
   aws cloudformation create-stack ...

**Common failure causes:**

• IAM permission errors → verify `--capabilities CAPABILITY_IAM` and role trust policies
• Resource limit exceeded → reques